This paper is in the following e-collection/theme issue:

Wearables and MHealth Reviews (310) mHealth for Wellness, Behavior Change and Prevention (3311) Diagnostic Tools in Mental Health (326) Digital Biomarkers and Digital Phenotyping (283) Methods and New Tools in Mental Health Research (558) mHealth for Diagnosis (80) mHealth for Screening (296) Voice Assistants (51) Depression and Mood Disorders; Suicide Prevention (1687) Speech and Phonetics in Verbal Protocols (Think Aloud, Recall, and Explanation Tasks) (95)

Published on in Vol 9, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24352, first published .
Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

Authors of this article:

Olivia Flanagan1 Author Orcid Image ;   Amy Chan2 Author Orcid Image ;   Partha Roop3 Author Orcid Image ;   Frederick Sundram1 Author Orcid Image
Article Authors Cited by (20) Tweetations (3) Metrics

Journals

  1. Kim A, Jang E, Lee S, Choi K, Park J, Shin H. Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach. Journal of Medical Internet Research 2023;25:e34474 View
  2. Tonn P, Seule L, Degani Y, Herzinger S, Klein A, Schulze N. Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study. JMIR Formative Research 2022;6(8):e37061 View
  3. Martínez-Nicolás I, Martínez-Sánchez F, Ivanova O, Meilán J. Reading and lexical–semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease. Scientific Reports 2023;13(1) View
  4. Gerczuk M, Triantafyllopoulos A, Amiriparian S, Kathan A, Bauer J, Berking M, Schuller B. Zero-shot personalization of speech foundation models for depressed mood monitoring. Patterns 2023;4(11):100873 View
  5. Calà F, Frassineti L, Sforza E, Onesimo R, D’Alatri L, Manfredi C, Lanata A, Zampino G. Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics. Bioengineering 2023;10(12):1375 View
  6. Olawade D, Wada O, Odetayo A, David-Olawade A, Asaolu F, Eberhardt J. Enhancing mental health with Artificial Intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health 2024;3:100099 View
  7. Shin J, Bae S. Use of voice features from smartphones for monitoring depressive disorders: Scoping review. DIGITAL HEALTH 2024;10 View
  8. Berisha V, Liss J. Responsible development of clinical speech AI: Bridging the gap between clinical research and technology. npj Digital Medicine 2024;7(1) View
  9. Mlakar I, Arioz U, Smrke U, Plohl N, Šafran V, Rojc M. An End-to-End framework for extracting observable cues of depression from diary recordings. Expert Systems with Applications 2024;257:125025 View
  10. Brown R, Bondy E, Prim J, Dichter G, Schiller C. The behavioral and physiological correlates of affective mood switching in premenstrual dysphoric disorder. Frontiers in Psychiatry 2024;15 View
  11. De Silva U, Madanian S, Olsen S, Templeton J, Poellabauer C, Schneider S, Narayanan A, Rubaiat R. Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders. Journal of Medical Internet Research 2025;27:e63004 View
  12. Provost E, Sperry S, Tavernor J, Anderau S, Yocum A, McInnis M. Emotion Recognition in the Real World: Passively Collecting and Estimating Emotions From Natural Speech Data of Individuals With Bipolar Disorder. IEEE Transactions on Affective Computing 2025;16(1):28 View
  13. Karlin B, Henry D, Anderson R, Cieri S, Aratow M, Shriberg E, Hoy M. Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance. JMIR AI 2025;4:e69149 View
  14. Crocamo C, Cioni R, Canestro A, Nasti C, Palpella D, Piacenti S, Bartoccetti A, Re M, Simonetti V, Barattieri di San Pietro C, Bulgheroni M, Bartoli F, Carrà G. Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study. JMIR Formative Research 2025;9:e65555 View
  15. Kerna N, Boulos A, Abreu M, Chigozie I, Fide-Nwoko F, Arube E, Eziechi E, Holets H, Pruitt K, Jomsky B, Chawla S. Current Applications of Artificial Intelligence in Psychiatry. Scientia. Technology, Science and Society 2025;2(4):125 View
  16. Leimhofer J, Petrovic M, Dominik A, Heider D, Hegerl U. Cross-Platform Availability of Smartphone Sensors for Depression Indication Systems: Mixed-Methods Umbrella Review. Interactive Journal of Medical Research 2025;14:e69686 View
  17. Lunetti C, Favini A, Trotta E. Can Artificial Intelligence Enhance European Emerging Adults’ Psychological Adjustment? A Scoping Review. Behavioral Sciences 2025;15(11):1483 View

Books/Policy Documents

  1. Jafar A, Shabnam S, Khan A. AI in Mental Health. View
  2. Plafky C, Badertscher H. Künstliche Intelligenz in der Sozialen Arbeit. View

Conference Proceedings

  1. Irfan S, Dhivya G, Raghavendran N, Babu M, Jasperline T, Saravanakumar R. 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). AI Personalized Mental Health Monitoring System using Machine Learning, and Natural Language Processing View